Churn Prediction

Save the customers you're about to lose

Identify the accounts most likely to churn — ranked by ARR, with the reasons behind the risk.

Outcome
32%

average reduction in churn for B2B SaaS customers

The problem

What's broken today

1

Customer Success operates on gut, gets the save call too late.

2

Health scores are color-coded vibes, not predictions.

3

Nobody can answer the CFO's "why are they leaving" question with confidence.

How Predict Labs solves it

Built for the way operators actually work

ARR-weighted risk ranking

We surface the accounts where saving them moves the number most — not just which ones might leave.

Driver-level explanations

Every prediction comes with the top 5 contributing signals, so CSMs walk into the call prepared.

Workflow integration

Push at-risk lists into your CRM, alert in Slack, or build a dashboard for the QBR.

Sample questions

Ask Predict Labs anything

Which enterprise customers are most likely to churn in the next 90 days?
Predict Labs
Building chart and analysis…
What's driving churn in our SMB segment?
Predict Labs
Building chart and analysis…
Show me at-risk accounts ranked by ARR
Predict Labs
Building chart and analysis…
How it works

Three steps to your first prediction

STEP 1

Connect product + billing data

Usage events, support tickets, billing history, NPS — we ingest the signals.

STEP 2

Train the model in minutes

Predict Labs auto-trains, validates, and ranks features. You get a model card you can defend.

STEP 3

Operationalize

Push scores into Salesforce, alert your CS team, watch save rate climb.

Getting started

What you'll need

These are the typical data sources for churn prediction. You don't need every source on day one — start with what you have.

Typical data sources
  • Billing/subscription data
  • Product usage events
  • Support ticket history
  • CRM account data

Try Churn Prediction on your own data

Free for 14 days. No credit card. No data scientist required.